$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

That command will generate code in `./src/client` and will use `axios` (the frontend HTTP library) internally.

### Try Out the Client Code

```console
$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

此命令将在 `./src/client` 中生成代码，并将在其内部使用 `axios`（前端HTTP库）。

### 尝试客户端代码

现在您可以导入并使用客户端代码，它可能看起来像这样，请注意，您可以为这些方法使用自动补全：

<img src="/img/tutorial/generate-clients/image02.png">

您还将自动补全要发送的数据：

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

Dieser Befehl generiert Code in `./src/client` und verwendet intern `axios` (die Frontend-HTTP-Bibliothek).

### Den Client-Code ausprobieren

```console
$ npm run generate-client

frontend-app@1.0.0 generate-client /home/user/code/frontend-app
> openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios
```

</div>

👈 📋 🔜 🏗 📟 `./src/client` &amp; 🔜 ⚙️ `axios` (🕸 🇺🇸🔍 🗃) 🔘.

### 🔄 👅 👩‍💻 📟

🔜 👆 💪 🗄 &amp; ⚙️ 👩‍💻 📟, ⚫️ 💪 👀 💖 👉, 👀 👈 👆 🤚 ✍ 👩‍🔬:

<img src="/img/tutorial/generate-clients/image02.png">

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      @Override
      public int size() {
        return axes.size();
      }

      @Override
      public E get(int axis) {
        checkElementIndex(axis, size());
        int axisIndex = getAxisIndexForProductIndex(index, axis);
        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      } else {
        // Generalize differing axis lengths to "variable"/"unknown".
        for (int axis_index = 0; axis_index < combined_shape.dims();
             ++axis_index) {
          int64_t axis_length = combined_shape.dim_size(axis_index);
          if (axis_length != component_shape.dim_size(axis_index)) {
            axis_length = -1;
          }
          TF_RETURN_IF_ERROR(

// Return a 1-D INT32 tensor containing a single value 1.
TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx);

// Return an op taking minimum of `input` long `axis` dimension.
TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis) {
  TF_Status* status = TF_NewStatus();

  TFE_Op* op = TFE_NewOp(ctx, "Min", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, input, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, axis, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TFE_TensorHandle* axis = TestAxisTensorHandle(ctx);
  TFE_Op* minOp = MinOp(ctx, input, axis);
  TFE_TensorHandle* retvals[1] = {nullptr};
  int num_retvals = 1;
  TFE_Execute(minOp, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(minOp);
  TFE_DeleteTensorHandle(input);
  TFE_DeleteTensorHandle(axis);
  ASSERT_EQ(1, num_retvals);